Immunosignature analysis of a ME/CFS patient. Part 1: viruses

Immunosignature analysis of a ME/CFS patient. Part 1: viruses

“Each hypothesis suggests its own criteria, its own means of proof, its own methods of developing the thruth; and if a group of hypotheses encompass the subject on all sides, the total outcome of means and of methods is full and rich.”

Thomas Chrowder Chamberlain, “The Method of Multiple Working Hypotheses” (Download)

The purpose of the following analysis is to search for the viral epitopes that elicited – in a ME/CFS patient – IgGs against a set of 6 peptides, determined thanks to an array of 150.000 random peptides of 16 amino acids each. These peptides were used as query sequences in a BLAST search against viral proteins. No human virus was found. Three phages of bacterial human pathogens were identified, belonging to the classes Actinobacteria and γ-Proteobacteria. One of these bacteria, Serratia marcescens, was identified in a similar study on 21 ME/CFS cases.  

(a commentary in Dutch is available here)

1. The quest for a pathogen

Scientists have been speculating about an infectious aetiology of ME/CFS for decades, without ever being able to link the disease to a specific pathogen. The idea that the disease might be triggered and/or maintained by an infection is due to the observation that for most of the patients the onset occurs after an infectious illness (Chu, L. et al. 2019). It has also been observed that after a major infection (whether parasitic, viral or bacterial) about 11% of the population develops ME/CFS (Mørch K et al. 2013), (Hickie I. et al. 2006).

In recent years, the advent of new technologies for pathogen hunting has given renewed impulse to the search for ongoing infection in this patient population. A 2018 study, investigating the genetic profile of peripheral blood for prokaryotic and eukaryotic organisms reported that most of the ME/CFS patients have DNA belonging to the eukaryotic genera Perkinsus and Spumella and to the prokaryotic class β-proteobacteria (alone or in combination) and that these organisms are statistically more present in patients than in controls (Ellis J.E. et al. 2018). Nevertheless, a previous metagenomic analysis of plasma by another group revealed no difference in the content of genetic material from bacteria and viruses between ME/CFS patients and healthy controls (Miller R.R. et al. 2016). Moreover, metagenomic analysis pursued in various samples from ME/CFS patients by both Stanford University and Columbia University has come empty (data not published, R, R).

2. Immunological methods

Another way of investigating the presence of current and/or past infections that might be specific of this patient population is to extract the information contained in the adaptive immune response. This can be made in several ways, each of them having their own limits. One way would be to collect the repertoire of T cell receptors (TCRs) of each patient and see if they have been elicited by some particular microorganism. This is a very complex and time-consuming method that has been developed in recent years and that I have described in details going through all the recent meaningful publications (R). The main limitation of this method is that, surprisingly, TCRs are not specific for a single epitope (Mason DA 1998), (Birnbaum ME et al. 2014), so their analysis is unlikely to reveal what agent selected them. On the other hand, the advantage of this method is that T cell epitopes are linear ones, so they are extremely suited for BLAST searches against protein databases. An attempt at applying this method to ME/CFS is currently underway: it initially gave encouraging results (R), then rejected by further analysis.

Another possible avenue for having access to the information registered by adaptive immunity is to investigate the repertoire of antibodies. The use of a collection of thousands of short random peptides coated to a plate has been recently proposed as an efficient way to study the response of B cells to cancer (Stafford P. et al. 2014), infections (Navalkar K.A. et al. 2014), and immunization (Legutki JB et al. 2010). This same method has been applied to ME/CFS patients and it has shown the potential of identifying an immunosignature that can differentiate patients from controls (Singh S. et al. 2016), (Günther O.P. et al. 2019). But what about the antigens eliciting that antibody profile? Given a set of peptides one’s antibodies react to, a possible solution for interpreting the data is to use these peptides as query sequences in a BLAST search against proteins from all the microorganisms known to infect humans. This has been done for ME/CFS, and the analysis led to several matches among proteins from bacteria, viruses, endogenous retroviruses and even human proteins (in fact, both this method and the one previously described can detect autoimmunity as well) (Singh S. et al. 2016).  There are several problems with this approach, though. First of all, the number of random peptides usually used in these arrays is not representative of the variety of possible epitopes of the same length present in nature. If we consider the paper by Günther O.P. and colleagues, for instance, they used an array of about 10^5 random peptides with a length of 12 amino acids each, with the number of all the possible peptides of the same length being  20^12 ∼ 4·10^15. This means that many potential epitopes one has antibodies to are not represented in the array. Another important limitation is that B cell epitopes are mainly conformational ones, which means that they are assembled by the folding of the proteins they belong to (Morris, 2007), the consequence of this being that the subset of random peptides one’s serum react to are in fact linear epitopes that mimic conformational ones (they are often called mimotopes) (Legutki JB et al. 2010). This means that a BLAST search of these peptides against a library of proteins from pathogens can lead to completely misleading results.

Recently an array of overlapping peptides that cover the proteins for many know viruses has been successfully used for the study of acute flaccid myelitis (AFM). This technology, called VirScan, has succeeded in linking AFM to enteroviruses where metagenomic of the cerebrospinal fluid has failed (Shubert R.D. et al. 2019). This kind of approach is probably better than the one employing arrays of random peptides, for pathogen hunting. The reason being that a set of only 150.000 random peptides is unlikely to collect a significant amount of B cell epitopes from viruses, bacteria etc. Random peptides are more suited for the establishment of immunosignatures.

3. My own analysis

I have recently got access to the results of a study I was enrolled in two years ago. My serum was diluted and applied to an array of 150.000 peptides with a length of 16 random amino acids (plus four amino acids used to link the peptides to the plate). Residues Threonine (T), Isoleucine (I) and Cysteine (C) were not included in the synthesis of peptides. Anti-human-IgG Ab was employed as a secondary antibody. The set of peptides my IgGs reacted to has been filtered with several criteria, one of them being subtracting the immune response common to healthy controls, to have an immune signature that differentiates me from healthy controls. The end result of this process is the set of the following six peptides.


Table 1. My immunosignature, as detected by an array of 150.000 random peptides 20-amino-acid long, four of which are used for fixing them to the plate and are not included here.

The purpose of the following analysis is to search for the viral epitopes that elicited this immune response. To overcome the limitations enumerated at the end of the previous paragraph I have decided to search within the database of viral proteins for exact matches of the length of 7 amino acids. Why this choice? A survey of a set of validated B cell epitopes found that the average B cell epitope has a linear stretch of 5 amino acids (Kringelum, et al., 2013); according to another similar work, the average linear epitope within a conformational one has a length of 4-7 amino acids (Andersen, et al., 2006). To filter the matches and to reduce the number of matches due to chance, I opted for the upper limit of this length. I excluded longer matches to limit the number of mimotopes for conformational epitopes. Moreover, I decided to look only for perfect matches (excluding the possibility of gaps and substitutions) so to simplify the analysis. It is worth mentioning that a study of cross-reactive peptides performed for previous work (Maccallini P. 2016), (Maccallini P. et al. 2018) led me to the conclusion that cross-reactive 7-amino-acid long peptides might often have 100% identity.

Figure 1. For each match, the matching protein and the organism it belongs to are reported. The protein ID has a link to the NCBI protein database, while the name of the organism has a link to the NCBI taxonomy browser. The host of the microorganism is also indicated, as well as its habitat, with links to further information.

So, to recap, I use the following method: BLAST search (blastp algorithm) against viral proteins (taxid 10239), a perfect match (100% identity) of at least 7-amino-acid peptides (≥43% query cover), max target sequences: 1000, substitution matrix: BLOSUM62.

4. Results

Table 2 is a collection of the matches I found with the method described above. You can look at figure 1 to see how to read the table.

ALHHRHVGLRVQYDSG (102_1_F_viruses)
Prokaryotic dsDNA virus sp.
Archea, Ocean
Terrestrivirus sp
Amoeba, forest soil
ALHRHRVGPQLQSSGS (102_2_F_viruses)
Stenotrophomonas phage vB_SmaS_DLP_5
Stenotrophomonas maltophilia (HP)
ALHRRQRVLSPVLGAS (102_3_F_viruses)
QHN71154.1 (288-294)
Mollivirus kamchatka
Protozoa (R)
QDB71078.1 (24-30)
Serratia phage Moabite
Serratia marcescens (HP)
ALHRVLSEQDPQLVLS (102_4_F_viruses)
BAR30981.1 (151-157)
uncultured Mediterranean phage uvMED
Archea and Bacteria, Med. sea
AXS67723.1 (494-500)
Cryptophlebia peltastica nucleopolyhedrovirus
YP_009362111.1 (74-80)
Marco virus
Ameiva ameiva
ALHLHRHVLESQVNSL (102_6_F_viruses)
YP_009119106.1 (510-516)
Pandoravirus inopinatum
ASZ74651.1 (61-67)
Mycobacterium phage Phabba
Mycobacterium smegmatis mc²155 (HP)

Table 2. Collection of the matches for the BLAST search of my unique set of peptides against viral proteins (taxid 10239). HP: human pathogen. See figure 1 for how to read the table.

5. Discussion

There are no human viruses detected by this search. There are some bacteriophages and three of them have as hosts bacteria that are known to be human pathogens. Bacteriophages (also known as phages) are viruses that use the metabolic machinery of prokaryotic organisms to replicate (figure 2). It is well known that bacteriophages can elicit specific antibodies in humans: circulating IgGs to naturally occurring bacteriophages have been detected (Dąbrowska K. et al. 2014) as well as specific antibodies to phages injected for medical or experimental reasons (Shearer WT et al. 2001), as reviewed here: (Jonas D. Van Belleghem et al. 2019). According to these observations, one might expect that when a person is infected by a bacterium, this subject will develop antibodies not only to the bacterium itself but also to its phages.

Figure 2. Half of all viruses have an almost regular icosahedral shape, but several phages present an irregular icosahedral shape, with a prolate capsid (Luque and Reguera 2013). On the left a wrong representation of a phage. It is wrong because the capsid has 24 faces, instead of 20. On the right, the representation of a regular icosahedron made by Leonardo Da Vinci for De Divina Proportione, a mathematical book by Luca Pacioli.

If that is the case, we can use our data in table 2 to infer a possible exposure of our patient to the following bacterial pathogens: Stenotrophomonas maltophilia (HP), Serratia marcescens (HP), Mycobacterium smegmatis mc²155 (HP). In brackets, there are links to research about the pathogenicity for humans of each species. M. smegmatis belongs to the class Actinobacteria, while S. maltophila and S. marcescens are included in the class γ-Proteobacteria.

Interesting enough, Serratia marcescens was identified as one of the possible bacterial triggers for the immunosignature of a group of 21 ME/CFS patients, in a study that employed an array of 125.000 random peptides (Singh S. et al. 2016). This bacterium accounts for rare nosocomial infections of the respiratory tract, the urinary tract, surgical wounds and soft tissues. Meningitis caused by Serratia marcescens has been reported in the pediatric population (Ashish Khanna et al. 2013).

Mollivirus kamchatka is a recently discovered giant virus whose hosts are presumed to be protozoa that inhabit the soil of subarctic environment (Christo-Fourox E. et al. 2020). Not sure what the meaning might be in this context.

6. Next step

The next step will be to perform a similar BLAST search against bacterial proteins to see, among other things,  if I can find matches with the six bacteria identified by the present analysis. A further step will be to pursue an analogous study for eukaryotic microorganisms and for human proteins (in search for autoantibodies).

Mark Davis e il test immunitario universale

Mark Davis e il test immunitario universale

1. Introduzione

Queste sono solo alcune note raccolte dal discorso che Mark Davis ha pronunciato in occasione del Community Symposium tenutosi nell’agosto scorso (2017) a Stanford (video). Nei paragrafi 2, 3, 4 e 5 introdurrò alcune nozioni di base sui recettori delle cellule T (T cell receptors: TCR); il paragrafo 6, attraverso riferimenti  al video già menzionato e a tre articoli pubblicati da Davis et al. nel corso degli ultimi quattro anni, descrive una  nuova tecnologica sviluppata da Mark Davis e colleghi. Questi cenni preliminari dovrebbero auspicabilmente fornire i mezzi per comprendere a pieno la portata dei dati pilota presentati da Mark Davis a proposito dell’attività delle cellule T nella ME/CFS (paragrafo 7) e nella malattia di Lyme cronica (paragrafo 8), mostrando perché tale tecnologia prometta di divenire una sorta di test universale per qualsiasi tipo di infezione o patologia autoimmune, nota o sconosciuta.

2. Cellule T

I linfociti T sono una tipologia di leucociti (o globuli bianchi), vale a dire la componente cellulare del nostro sistema immunitario. La gran parte dei linfociti T in circolo è rappresentata da linfociti T helper (T helper cells: Th cells)  e da linfociti T citotossici (cytotoxic T lymphocytes: CTL). Mentre la funzione dei linfociti T helper è quella di regolare l’attività degli altri leucociti attraverso la produzione di un’ampia gamma di trasmettitori chimici (le citochine, cytokines), le CTL sono coinvolte direttamente nella soppressione delle cellule ospiti infette. I linfociti T appartengono al ramo cosiddetto adattivo del sistema immunitario, assieme alle cellule B (le fabbriche di anticorpi), e, in quanto tali, il loro compito è quello di garantire una difesa specifica, su misura, contro gli agenti patogeni: per contrastare uno specifico agente patogeno, il nostro sistema immunitario può schierare in campo non solo anticorpi specifici ma anche specifici linfociti T (Th cells e CTL). Il ramo innato del sistema immunitario, invece, (nel quale rientrano le cellule natural killer, i macrofagi, le cellule dendritiche, i mastociti…) è in grado di fornire soltanto una difesa aspecifica, una prima linea di risposta immunitaria.

3. Recettori dei linfociti T

I linfociti T sono in grado di andare alla ricerca di specifici patogeni grazie a una molecola espressa sopra la propria superficie, chiamata recettore del linfocita T (TCR). Nella figura 1 si può vedere una schematica rappresentazione del TCR e del meccanismo in virtù del quale il linfocita T riconosce il proprio target. Gli antigenti (proteine) degli agenti patogeni vengono indicati ai linfociti T da altre cellule del nostro corpo: vengono esposte sopra molecole chiamate Complesso Maggiore di Istocompatibilità (MHC), che si trova espresso sulla membrana esterna. Se un dato antigene mostra compatibilità con il TCR di uno specifico linfocita T, tale linfocita T si attiva e comincia a proliferare (espansione clonale, clonal expansion). Le due catene principali (α and β) sono assemblate combinando la trascrizione di segmenti di gene, ognuno dei quali ha copie multiple, leggermente diverse fra loro: in altre parole, i TCR vengono assemblati a partire da peptidi scelti a caso da un insieme di diverse alternative possibili. Questo comporta un repertorio di 10^15 diversi possibili TCR  (Mason DA 1998). Ogni linfocita T mostra un solo tipo di TCR.


4. Cellule T helper 

Le cellule T helper sono programmate per riconoscere esclusivamente antigeni esposti dalle molecole MHC di seconda classe (II): questa classe di MHC viene espressa sulla membrana esterna di alcuni leucociti, principalmente le cellule dendritiche, le cellule B e i macrofagi (tutte assieme dette “cellule che presentano l’antigene”, antigen presenting cells: APC). Le molecole MHC II legano il TCR delle cellule T helper grazie al peptide CD4 (espresso unicamente dalle cellule T helper). L’antigene presentato dalle molecole MHC è un peptide lungo 13-17 amminoacidi (Rudensky, et al., 1991) (figura 2).


5. Linfociti T citotossici 

I TCR espressi dai linfociti T citotossici (CTL) possono legare solo antigeni esposti dalle molecole MHC di prima classe (I), che si trovano nella membrana esterna di qualunque cellula del nostro corpo. La glicoproteina CD8 è la molecola che rende i TCR espressi dalle CTL specifici per il MHC I. Mentre gli antigeni esposti dalle APC appartengono a patogeni raccolti sul campo di battaglia di passate infezioni, i peptidi esposti dal MHC I di una specifica cellula appartengono a patogeni che hanno fatto ingresso nella cellula stessa, e pertanto costituiscono la prova di un’infezione intracellulare ancora in atto (figura 3). Nel momento in cui un CTL riconosce un antigene che combacia con il proprio TCR, il CTL iduce l’apoptosi (morte programmata) della cellula che mostra l’antigene. Gli antigeni esposti dal MHC I sono peptidi che vanno dagli 8 ai 10 amminoacidi (Stern, et al., 1994).MHC I.JPG

Figure 3. Una cellula infetta espone un antigene virale sul proprio MHC I. Il TCR di un CTL si lega a questo peptide ed invia un segnale interno diretto al suo proprio nucleo, il quale risponde attivando l’apoptosi (attraverso il rilascio di granzimi, ad esempio) della cellula infetta (disegno di Paolo Maccallini).6. Il test immunologico universale

Nel corso del suo discorso, Mark Davis illustra alcuni concetti base sul sistema immunitario, prima di passare a introdurre i nuovi, entusiasmanti dati riguardo alla ME/CFS e alla Lyme cronica (o post-treatment Lyme disease syndrome: PTLDS). Contestualmente, però, dedica alcuni minuti alla descrizione di un complesso nuovo test che teoricamente renderebbe possibile estrapolare tutte le informazioni contenute nel repertorio di TCR presenti – in un dato momento – nel sangue di un essere umano. Un test del genere – che chiamerei “test immunologico universale” – avrebbe la capacità di determinare se un paziente presenta un’infezione in corso (e, nel caso, indicare il patogeno coinvolto) oppure una malattia autoimmune (anche qui specificando la natura dell’autoantigene, ossia il tessuto attaccato dal sistema immunitario). A quanto mi è dato di comprendere, il test richiede tre passaggi, che elenco nelle sezioni seguenti.

6.1. Primo step: sequenziamento del TCR

Come già spiegato nel paragrafo 3, quando un linfocita T incontra un peptide a cui è compatibile, comincia a proliferare; pertanto, nel sangue di un paziente con infezione in corso (o con reazione contro il proprio organismo, cioè con reazione autoimmune) è possibile trovare molteplici copie di linfociti T che esprimono il medesimo TCR: a differenza dei controlli sani, nei quali circa il 10% delle CD8 totali è rappresentato da copie di pochi diverse linfociti T (figura 4, prima linea), nei pazienti affetti da Lyme incipiente –  un esempio di infezione attiva – o sclerosi multipla (MS) –  un esempio di malattia autoimmune – abbiamo una massiccia clonazione di alcune linee di CTL (figura 5, seconda e terza riga, rispettivamente). Il primo step del test immunologico universale starà allora nell’identificazione dell’esatta sequenza di TCR espressa dai linfociti T presenti nel sangue, come si legge in Han A et al. 2014, dove troviamo descritto il sistema per sequenziare i geni delle catene α e β di un dato linfocita T. Tale approccio permette di costruire grafici come quello in figura 4 e quindi permette di determinare se il paziente presenti in atto un’attività anomala dei linfociti T oppure no. Qualora si riscontri un fenomeno di espansione clonale, è legittimo ipotizzare che stia avendo luogo o un’infezione o una condizione autoimmune di qualche sorta.

Clonal expansion CD8
Figure 4. Ogni cerchio rappresenta un paziente. Nella prima riga vediamo quattro controlli sani, che non presentano affatto espansione clonale delle cellule CD8 (come nel primo paziente da sinistra) oppure la presentano in maniera assai moderata (come indicato dalle porzioni in blu, bianco e grigio). Nella seconda riga troviamo invece quattro pazienti con malattia di Lyme attiva (fase incipiente) e possiamo ben notare come ciascuno di loro, nessuno escluso, presenti espansione clonale di solo tre diverse T cells (porzioni in rosso, blu e arancione). Nella terza riga, infine, abbiamo quattro pazienti affetti da MS, le cui cellule CD8 sono per maggior parte rappresentate da cloni di una selezione ristretta di T cells.
Fonte: slide proposte da Mark Davis durante il Community Symposium.

6.2. Secondo step: raggruppamento dei TCR 

Mark Davis e colleghi hanno realizzato un software capace di identificare i TCR che condividono il medesimo antigene, sia in un singolo individuo che trasversalmente a un gruppo. L’algoritmo è stato denominato GLIPH (grouping of lymphocyte interaction by paratope hotspots) ed ha dato prova di poter raggruppare – per fare un esempio – i recettori  dei linfociti T CD4 di 22 soggetti con infezione da M. tuberculosis latente in 16 gruppi distinti, ognuno dei quali comprende TCR provenienti da almeno tre individui (Glanville J et al. 2017). Cinque di questi gruppi sono riportati nella figura 5. L’idea sottostante è che TCR che appartengono allo stesso raggruppamento reagiscano allo stesso complesso peptide-MHC (pMHC).

Figure 5.  Cinque gruppi di TCR provenienti da 22 diversi pazienti affetti da turbercolosi latente, raggruppati grazie al GLIPH. La prima colonna da sinistra riporta l’identificativo dei TCR; la seconda l’identificativo dei pazienti. Le CDR per le catene β e α si trovano, rispettivamente, sulla terza e sulla quinta colonna. Tratto da Glanville J et al. 2017.

6.3. Terzo step: ricerca degli epitopi

Come abbiamo visto, questa nuova tecnologia consente di rilevare se sia in atto un’espansione clonale di linfociti T sequenziando i TCR dal sangue periferico. Consente inoltre di raggruppare i TCR presenti in un singolo paziente o condivisi da più pazienti. Il passaggio successivo è quello di identificare a quale/i tipo/i di antigene ognuno di questi raggruppamenti reagisca. Infatti, se potessimo identificare degli antigeni comuni in un gruppo di pazienti dai sintomi comparabili nei quali si riscontri un’espansione clonale in atto e simili TCR, saremmo messi in grado di comprendere se il loro sistema immunitario stia attaccando un patogeno (e di identificare il patogeno) o se stia piuttosto attaccando un tessuto ospite e, qualora fosse questo il caso, di identificare il tessuto. Come già detto, il numero di possibili combinazioni per il materiale genetico dei TCR è calcolato attorno ai 10^15, ma il numero dei possibili epitopi di cellule Th è circa 20^15, che corrisponde a più di 10^19. Ciò implica che i TCR debbano essere in una qualche misura cross-reattivi se vogliono essere in grado di riconoscere tutti i possibili peptidi esposti dai MHC (Mason DA 1998). Il grado di tale cross-reattività e il meccanismo attraverso il quale viene ottenuta sono stati spiegati con esattezza da Mark Davis e colleghi in un recente articolo (Birnbaum ME et al. 2014), che ci fornisce il terzo step del test immunologico universale. Lo scopo di questa fase consiste nel prendere un dato TCR e trovare il repertorio dei suoi specifici antigeni (giova ripetere che, appunto, ogni TCR reagisce a più antigeni). Per comprendere come ciò sia possibile, guardiamo a uno degli esperimenti descritti nell’articolo più sopra citato. I ricercatori si sono concentrati su due TCR ben precisi (chiamati Ob.1A12 e Ob.2F3), clonati da un paziente con MS e noti per essere capaci di riconoscere i pepetidi 85-99 (figura 6) della proteina basica della mielina (MBP) esposti dall’ HLA-DR15. Hanno poi preparato un insieme di cellule di lievito che esprimono molecole HLA-DR15, ognuna caratterizzata da un diverso peptide formato da 14 amminoacidi, con amminoacidi fissi esclusivamente alle posizioni 1 e 4, dove il peptide è ancorato al MHC (figura 6, sinistra). Quando alla coltura di cellule di lievito  che esprimono complessi pMHC vengono aggiunte copie di Ob.1A12, queste legano solo con alcune di quelle e, come è possibile vedere dalla parte destra della figura 6, per ciascuna posizione degli epitopi legati dal Ob.1A12 esiste un amminoacido con maggior tasso di probabilità: ad esempio, l’epitopo Ob.1A12 tipico presenta preferibilmente alanina (A) in posizione -4, istidina (H) in posizione -3, arginina (R) in posizione -2, e così via. Da notare che istidina (H) in posizione 2 e fenilanina (F) in posizione 3 sono amminoacidi obbligatori per un epitopo di  Ob.1A12.

Ob. 1A12
Figure 6. Sulla sinistra: il peptide 85-99 della proteina basica della mielina (myelin basic protein, MPB) è risaputo essere un epitopo per il TCR Ob.1A12. In posizione 1 e 4 possiede due residui che gli consentono di legare con la molecola MHC. In posizione -2, -1, 2, 3 3 5 troviamo invece i residui che legano con il TCR. La seconda riga rappresenta l’epitopo generico della libreria peptidica utilizzata per identificare il grado di cross-reattività tra tutti i possibili epitopi di Ob.1A12. A destra: la probabilità di ciascun amminoacido per ciascuna posizione è rappresentata da sfumature di viola. Come potete vedere, l’istidina (H) in posizione 2 e la fenilalanina (F) in posizione 3 sono amminoacidi obbligatori affinché un epitopo sia reattivo con Ob.1A12. Da (Birnbaum ME et al 2014).

La tabella sulla destra della figura 6 rappresenta, infatti, una tabella di sostituzione (substitution matrix) di dimensioni 14×20, uno strumento impiegato per scansionare il database dei peptidi in modo da trovare, tra tutti i peptidi conosciuti espressi da creature viventi, tutti i possibili epitopi specifici per Ob.1A12. Le matrici di sostituzione vengono solitamente utilizzate nel cosiddetto allineamento di peptidi (peptide alignment), una tecnica che punta all’identificazione di similitudini tra peptidi. Tali matrici sono basate su considerazioni di tipo evoluzionistico (Dayhoff, et al., 1978) o sullo studio delle regioni conservate delle proteine (Henikoff, et al., 1992). Ma la ricerca degli epitopi specifici di un dato TCR richiede (come abbiamo visto per Ob.1A12) una matrice di sostituzione costruita ad hoc per quel TCR: ogni TCR richiede la propria matrice di sostituzione, ottenuta incubando cellule T esprimenti quel TCR con una coltura di lieviti che espongono sui propri MHC una grande varietà di peptidi casuali, e analizzando poi i dati ricavati dall’esperimento. Quindi, un processo piuttosto complesso! Nel caso di Ob.1A12, questo processo ha portato a 2330 peptidi (incluso MBP), mentre la matrice di sostituzione specifica per Ob.2F3 ha trovato 4824 epitopi all’interno dell’intero database di peptidi. Questi peptidi includevano sia proteine non umane (batteriche, virali…) che peptidi umani. Per 33 di loro (26 non umani e 7 proteine umane), questo gruppo di ricercatori ha eseguito un test per verificare direttamente la previsione: 25/26 dei peptidi ambientali e 6/7 dei peptidi umani hanno indotto la proliferazione di cellule T che esprimono il TCR Ob.1A12 e/o il Ob.2F3, e questa è una prova della validità di questa analisi! Questi 33 peptidi sono riportati nella figura 7. Questo è l’ultimo passaggio del test immunitario universale, quello che dal TCR conduce agli epitopi. Come avete visto, un enorme insieme di diversi peptidi da diverse fonti reagisce con un singolo tipo di TCR; in altre parole, la cross-reattività è una proprietà intrinseca del TCR. Ciò significa anche che i test di trasformazione linfocitaria (LTT), ampiamente utilizzati in Europa per l’individuazione di infezioni da Borrelia burgdorferi e altri patogeni, comportano un rischio elevato di risultati falsi positivi e richiedono un processo di validazione sperimentale e teorica, attualmente mancante.

Crossreactive epitopes
Figura 7. Una serie di 33 peptidi che si suppongono essere epitopi specifici sia per Ob.1A12 che per Ob.2F3. Tratto da Birnbaum ME et al. 2014.

Siamo ora pronti ad apprezzare appieno i dati pilota che Mark Davis ha presentato al Symposium sull’espansione clonale delle cellule T CD8 nella ME/CFS e nella Lyme cronica.

7. “We have a hit!”

Mark Davis, insieme a Jacob Glanville e José Montoya, hanno sequenziato TCR dal sangue periferico di 50 pazienti ME/CFS e 49 controlli (primo passo del test immunitario universale, ricordate?), quindi li hanno raggruppati usando l’algoritmo GLIPH (secondo passo). Hanno trovato 28 cluster, ciascuno costituito da più di 2500 sequenze simili, e ogni cluster raccoglie sequenze multiple dallo stesso individuo e sequenze (che sono forse più importanti) da pazienti diversi (figura 8). Il cluster che ho cerchiato in rosso, ad esempio, è una raccolta di oltre 3000 TCR simili. La presenza di questi ampi cluster nei pazienti ME/CFS, rispetto ai controlli sani, rappresenta una prova indiretta di una risposta specifica delle cellule T a un trigger comune in questo gruppo di pazienti, che potrebbe essere un agente patogeno o un tessuto del corpo (o tutti e due).

Clustered TCR
Figura 8. Nella ME/CFS le sequenze di TCR ricavati da 50 pazienti formano 28 raggruppamenti che presentano più di 2500 sequenze simili – cosa che assolutamente non avviene nei controlli sani. Questo fa pensare ad una qualche risposta immunitaria ad un patogeno o ad un tessuto umano (o entrambi). Fonte: slide proposta da Mark Davis durante il Community Symposium.

Tra questi 50 pazienti ME/CFS, Davis e colleghi hanno selezionato 6 pazienti con geni HLA simili (figura 9, sinistra), 5 femmine tra loro, e hanno studiato i loro TCR più in profondità. Nella metà destra della figura 9, è possibile vedere per ciascun paziente il grado di espansione clonale delle CTL. Ricordate che nei controlli sani solo circa il 10% dei CTL è composto da cloni di alcune cellule (figura 4, prima riga), mentre qui vediamo che circa il 50% di tutti i CTL è composto da cloni. Quindi, una “marcata espansione clonale” delle cellule T CD8, come ha detto Davis.

ME subjects CD8
Figura 9. A sinistra: sono stati selezionati 6 pazienti ME/CFS con HLA simili. Sulla prima colonna da sinistra sono stati riportati gli identificativi dei pazienti; la seconda colonna ci informa sull’età di ciascuno; la terza sul genere; la quarta avvisa di eventuali esposizione a citomegalovirus; la quinta riguarda i geni del MHC I. A destra: l’analisi dell’espansione clonale delle cellule T CD8 per ognuno dei pazienti. L’espansione clonale è marcata (circa al 50%), se comparata a quella dei controlli sani (circa al 10%).

Le sequenze delle catene α e β dei TCR di tre dei sei pazienti (pazienti L4-02, L4-10 e L3-20) sono riportate in figura 10 (ho verificato che effettivamente si tratta di catene α e β di TCR umani, inserendole in BLAST).

TCR epitope
Figura 10. Catene β (prima colonna) e rispettive catene α (quinta colonna) provenienti da tre pazienti ME/CFSchains  (L4-02, L4-10, and L3-20, ultima colonna).

Quindi, abbiamo visto finora i primi due passaggi del test immunitario universale. E il terzo passo? Nel suo discorso, Mark Davis non ha presentato alcun particolare epitopo, ha solo mostrato una diapositiva con quella che probabilmente è la selezione degli epitopi dalla libreria discussa nel paragrafo 6.3 da parte di uno dei TCR riportati in figura 10. Questa selezione è riportato in figura 11, ma da quella foto non è possibile raccogliere alcuna informazione sull’identità di questi epitopi. Come probabilmente ricorderete dal paragrafo 6.3, l’analisi dei peptidi selezionati da un TCR nella libreria di peptidi  consente l’identificazione di una matrice di sostituzione che può essere utilizzata per selezionare tutti i possibili epitopi di quel TCR specifico, dal database delle proteine. Quest’ultima fase cruciale deve essere ancora eseguita, o è già stata eseguita, ma Davis non ha comunicato i risultati preliminari durante il suo discorso. Recentemente sono state messe a disposizione nuove risorse dalla Open Medicine Foundation, affinché questa ricerca promettente possa essere ulteriormente perseguita (R). Lo scopo qui, come già detto, è di trovare l’antigene che innesca questa risposta delle cellule T. Come ha detto Mark Davis, potrebbe essere un antigene di un agente patogeno specifico (forse un patogeno comune che va e viene) che suscita una risposta immunitaria anomala che finisce per colpire alcuni tessuti ospiti (microglia, per esempio), portando così attivazione immunitaria che è stata recentemente segnalata da Mark Davis stesso e altri in ME/CFS (Montoya JG et al. 2017). L’idea di un patogeno comune che innesca una risposta immunitaria patologica non è nuova in medicina, e la febbre reumatica (RF) è un esempio di una tale malattia: la RF è una malattia autoimmune che attacca il cuore, il cervello e le articolazioni ed è generalmente innescata da uno streptococco che infetta la gola (Marijon E et al. 2012). L’altra possibilità è, naturalmente, quella di un’infezione in corso di qualche tipo, che deve ancora essere rilevata. Come detto (vedi par. 6.1), l’espansione clonale delle cellule T CD8 è presente sia nelle infezioni acute (come la malattia di Lyme) che nelle malattie autoimmuni (come la SM) (figura 4), quindi dobbiamo aspettare l’identificazione dell’antigene se vogliamo capire se l’attività del CTL è contro un agente patogeno e/o contro un tessuto del nostro corpo.

peptide library
Figura 11. Nella figura possiamo osservare la selezione, che avviene in più momenti, di una serie di peptidi da parte di un particolare TCR proveniente da un paziente ME/CFS. La selezione ha luogo tra una enorme quantità di peptidi esposti dall’ HLA-A2 (MHC I) espresso da cellule di lievito. Ad ogni passaggio il numero di possibili peptidi si riduce.

8. La Lyme cronica esiste

È stato probabilmente trascurato il fatto che nel suo discorso, Mark Davis ha riportato anche dati molto interessanti sulla sindrome della malattia di Lyme post-trattamento (PTLDS, nota anche come malattia di Lyme cronica). In particolare, ha trovato un’espansione clonale marcata nelle cellule T CD8 di 4 pazienti PTLDS (circa il 40% dei CTL totali) come riportato nella figura 12: si consideri che in questo caso le fette blu rappresentano cellule T uniche, mentre tutte le altre fette rappresentano cloni! Tutto ciò che è stato detto sull’espansione clonale CD8 nella ME/CFS si applica anche in questo caso: potrebbe essere la prova di un’infezione in corso – forse la stessa B. burgdorferi, come suggerito da diversi modelli animali (Embers ME et al. 2017), (Embers ME et al. 2012), (Hodzic E et al. 2008), (Yrjänäinen H et al. 2010) –  o una coinfezione (un virus?). Oppure potrebbe essere l’espressione di una reazione autoimmune innescata dalla infezione iniziale. Questo deve ancora essere scoperto, eseguendo il test immunitario universale completo, ma ciò che è già chiaro dalla figura 12 è che la PTLDS è una condizione reale, con qualcosa di veramente anomalo nella risposta immunitaria: la Lyme cronica esiste.

Figura 12. Espansione clonale di cellule T CD8 in quattro pazienti affetti da Lyme cronica. L’espansione clonale, che indica l’attività delle cellule T contro un patogeno o un tessuto ospite, è assai marcata.

9. Conclusioni

Mark Davis e altri ricercatori hanno sviluppato un test complesso che è in grado di sequenziare i TCR dai pazienti, raggrupparli in gruppi di TCR che reagiscono agli stessi antigeni e scoprire gli antigeni che hanno attivato quella particolare risposta delle cellule T. Questo test è una sorta di test immunitario universale che è teoricamente in grado di riconoscere se una persona (o un gruppo di pazienti) presenta una risposta immunitaria contro un agente patogeno o contro uno dei loro stessi tessuti (o entrambe le cose). Questo approccio ha già fornito dati pilota su una attivazione anomala delle cellule T CD8 nei pazienti ME/CFS e nei pazienti PTLDS e, si spera, identificherà il trigger di questa risposta immunitaria nel prossimo futuro. Se la ME/CFS è causata da un’infezione attiva, da una malattia autoimmune o da entrambe le cose, il test immunologico universale potrebbe essere in grado di dircelo. Questa nuova tecnologia è per l’immunologia, ciò che il sequenziamento dell’intero genoma è per la genetica, o la metabolomica è per le malattie molecolari: non cerca un particolare agente patogeno o una particolare malattia autoimmune. No, cerca tutte le possibili infezioni e disturbi immunitari, anche quelli che devono ancora essere scoperti.

Why we can’t use LTTs, yet

A line of T cells (called Ob.2F3) expressing the same T cell receptor (TCR) from an MS patient was studied in 2014 and it was found to proliferate when incubated with 4824 different peptides. Thirty-three of them were further studied (see figure) and found to belong to both Homo sapiens and several different, unrelated microbes (Birnbaum ME et al. 2014). The taking home message here is that T cells are not specific to a single pathogen, they are highly cross-reactive, as it was already pointed out in this pivotal study: (Mason DA 1998). And this means that we can’t use lymphocyte transformation tests (LTTs) the way we do now. 

I feel really frustrated when patients send me their LTTs and ask me to comment the results. I have to say that they have vasted their money and that these results are useless. I do hope that my blog can make a difference and stop this unfair commerce at the expenses of desperate folks.

Crossreactive epitopes
Figure. A set of 33 peptides (both human and environmental) predicted to be specific epitopes for both Ob.1A12 and Ob.2F3. From (Birnbaum ME et al. 2014).






Three new possible autoepitopes in ME/CFS

Paolo Maccallini


I have performed a set of analysis of experimental data already published about autoimmunity to muscarinic receptors in ME/CFS. My predictions are that extracellular loop 2 and 3, and also transmembrane helix 5 of both muscarinic cholinergic receptors 4 and 3, are main autoantigens in a subset of ME/CFS patients. Moreover, I have found that autoimmunity to M4 and M3 ChR is independent from autoimmunity to beta 2 adrenergic receptor, also reported in ME/CFS patients.  


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease characterized by cognitive deficits, fatigue, orthostatic intolerance with symptoms exacerbated after exertion (post-exertional malaise, PEM) (IOM, 2015). This disease has no known cause but several abnormalities have been observed in energy metabolism (Tomas C. and Newton J. 2018), immune system, gut flora (Blomberg J. et al. 2018), brain (Zeineh MM. et al. 2014). A possible role for autoantibodies in the pathogenesis of the disease has been suggested by the finding of reactivity of patient sera to two nuclear antigens (Nishikai, et al., 1997), (Nishikai, et al., 2001), to cardiolipin (Hokama, et al., 2009), to HSP60 (Elfaitouri A. et al. 2013), and to muscarinic cholinergic (M ChR) and beta adrenergic receptors (ß AdR) (Tanaka S et al. 2003), (Loebel M et al. 2016); reactivity that was significantly elevated when compared to healthy contols. Reactivity to adrenergic and muscarinic Ch receptors has been confirmed by two independent groups, but these results have not been published yet (R). A role for autoantibodies in at least a subgroup of patients has also been suggested by a response to rituximab, a CD20 B cells depleting agent (Fluge Ø. et al. 2011), (Fluge Ø. et al. 20115), and to immunoadsorption (Scheibenbogen C. et al 2018). Sera response to muscarinic cholinergic receptors is confirmed in two studies but both of them used an immune assay with proteins coated on a plate. This kind of test does not allow to identify the exact autoepitope on the receptor and – even more importantly – it is subjected to false positive results because it exposes to sera surfaces of receptors that are hidden when they are in their physiological position (Ramanathan S et al 2016). Nevertheless, the amount of data provided in the study by Loebel et al. where reactivity of sera to 5 subtypes of muscarinic cholinergic receptors have been measured simultaneously, has – in our opinion – the potential to unveil the exact autoepitope(s). Thus, I performed a bioinformatical analysis on experimental data from this study in order to extract hidden information. I used a software for the in silico study of B cell epitope cross-reactivity (Maccallini P. et al. 2018) and a software for amino acid protrusion index calculation (Ponomarenko J. et al., 2008).  Our prediction is that patients sera mainly react to three epitopes that belong to the second and third extracellular loop of M3 and M4 ChR, but also to a hidden epitope of the same two receptors, leading to possible false positive results of this test. I have also found that the reactivity to beta 2 adrenergic receptor (ß2 AdR) found in the study by Loebel et al. is not due to the same antibody that reacts to muscarinic cholinergic receptors.


Search for cross-reactive epitopes. Cross-reactivity between muscarinic cholinergic receptors M4 and M3, and between M4 and M1 has been studied in silico using EPITOPE, a software already described (Maccallini P. et al. 2018). Briefly, EPITOPE searches for cross-reactive epitopes shared between two proteins (let’s say protein A and protein B) by comparing each possible 7-mer peptide of A with each possible 7-mer peptide of B. The comparison is made using the algorithm by Needleman and Wunsch (Needleman SB. and Wunsch CD. 1970)  with a gap model a + b·x, where a is the opening gap penalty, b is the extending one, and x is the extension of the gap. A penalty for gaps at the end of the alignment was also assumed. The choice for gap penalties and substitution matrix were done according to the theory already developed for peptide alignments (Altschul SF. 1991), (Karlin S. and Altschul SF. 1990). Available experimental data on cross-reactivity between γ enolase and α enolase (McAleese SM. et al. 1988)  have been used for EPITOPE calibration: a score >60 was considered the cut-off for cross-reactivity, a score below 50 indicates non-cross-reactive epitopes; a score between 50 and 60 defines a borderline result. A simpler version of EPITOPE has been used for single local alignments. The main program used for M4-M3 comparison, its subroutine NeWalign and the substitution matrix employed are available for download. Primary structures used in this work have been downloaded from UniProt and are the following ones: M1 ChR (P11229), M3 ChR (P20309), M4 ChR (P08173), B2 AdR (P07550).

Surface exposure. In order to select only those 7-mer peptides that are on the surface of proteins, I have considered their mean protrusion indexes. A protrusion index of at least 0.5 has been considered the cut-off for surface exposure. Protrusion indexes of single amino acids have been calculated with ElliPro. A protrusion index of 0.5 means that the amino acid is outside the ellipsoid of inertia which includes 50% of the centers of mass of all the amino acids of the protein (Ponomarenko J. et al., 2008). For M4 ChR I have used the crystal structure 5DSG (Thal DM. et al. 2016). The 3D structure of human M3 ChR has not been experimentally determined yet, so I have used a theoretical model built using murine M3 ChR (PDB ID: 4DAJA) as a template, provided by ModBase.

M ChR plot
Figure 1. The position of the first amino acid of each possible 7-mer peptide of M4 ChR is reported on the abscissa, the score for the comparison of each of these peptides with M1 ChR (blue line) and M3 ChR (orange line) is reported on the ordinate. N terminus, extracellular loop 1, 2 and 3 are also indicated. Scores above the yellow line indicate cross-reactivity, scores below the blue line indicate a lack of cross-reactivity.

Selection criteria. Our purpose is to predict whih epitopes of M3 and M4 ChRs sera from ME/CFS patients react to. So, we search for M4 ChRs 7-mer peptides that are cross-reactive to M3 ChR, but non-cross-reactive to M1 ChR. Moreover, they must present surface exposure both on M4 and on M3 ChR (otherwise antibodies can’t reach them). So, selection criteria for M4 ChR epitopes are as follows:

  1. they must be cross-reactive to M3 ChR;
  2. they must be non-cross reactive to M1 ChR or borderline;
  3. they must present a mean protrusion index ≥0.5;
  4. M3 ChR peptides they cross-react to have to present a mean protrusion index ≥0.5.

We will refer to strict criteria when we assume only non-cross-reactivity in 2, while weak selection criteria are fulfilled when M4 ChR epitopes have borderline reactivity to M3 Chr peptides.

M4 vs M1, M3
Figure 2. Distribution of the scores from the comparison of M4 ChR with M1 ChR (left) and with M3 ChR (right). M3 ChR presents a slightly higher mean score.


The search for 7-mer peptides of M4 ChR that are cross-reactive to M3 ChR found 108 sequences. We then studied cross-reactivity to M1 ChR for each of these peptides and we found that 11 of them are non-cross-reactive and that other 9  peptides have borderline reactivity. None of these 20 peptides presented a cross-reactivity to B2 AdR (Table 1S, column 1). Scores between peptides of M4 ChR and the other two muscarinic cholinergic receptors are plotted in Figure 1. The distribution of scores from the comparison of M3 ChR with M1 ChR and with M3 ChR are reported in Figure 2. For the M4 ChR 20 epitopes mentioned above, we calculated the mean protrusion indexes and we did the same calculation for their cross-reactive peptides on M3 ChR. We also indicated their position with respect to the plasma membrane. All these data are collected in Table 2S. Once we apply selection criteria on these 20 peptides, we obtain 9 epitopes (Table 1). Of these selected epitopes, one belongs to a transmembrane helix: peptide 186-192 of M4 ChR, which cross-reacts to peptide 231-237 of M3 ChR. Peptide 418-431 of M4 ChR is partially immersed in the plasmatic membrane, even though its cross-reactive peptide of M3 ChR is entirely exposed to the extracellular space, and the same applies to the other two epitopes found (figure 1). Peptide 175-181 of M4 ChR cross-reacts to peptide 211-217 of M3 ChR; peptide 186-192 of M4 Chr cross-reacts to peptide 222-228 of M3 ChR; peptide 418-431 of M4 Chr cross-reacts to peptide 513-522 of M3 ChR. Sequences that fulfill selection criteria and their respective inverted sequences are collected in  Table 2.

Table 1
Table 1. This is the collection of M4 Chr 7-mer peptides that are cross-reactive to M3 ChR; are not cross-reactive or borderline with M1 ChR; have a mean protrusion index higher than 0.5; are cross-reactive with epitopes of M3 ChR with a protrusion index higher than 0.5.


B cells autoimmunity to muscarinic cholinergic receptors in ME/CFS has been reported in two studies (Tanaka S et al. 2003), (Loebel M et al. 2016) and this finding has been recently confirmed by two other independent groups who have not published yet (R). The two studies mentioned used full-length proteins coated on a plate in order to perform the immune assay. With this kind of technique we may have both false positives (due to the fact that sera react with peptides that are not in the extracellular domain) and false negatives (due to protein denaturation, which leads to the formation of epitopes that would not be present if the protein were correctly folded) as it has been reported in the case of anti-MOG antibodies (Ramanathan S et al 2016). A way to solve the possible inaccuracy of these data would thus be to measure sera reactivity with a cell-based assay (CBA) which is a test where receptors are expressed by eukaryotic cells and thus they are held in their physiological position.

Figure 1. Peptides of table 1 that belong to the extracellular domain of M3 and M4 ChR are here highlighted directly on the 3D structures of their respective receptors.

Nevertheless, we can still try to extract hidden information from experimental data and predict the position of the epitope(s) ME/CFS patients sera react to. Knowing that sera from patients react to M4, M3 ChRs and that there is a low correlation between reactivity to M4 ChR and reactivity to M1 ChR (Loebel M et al. 2016), we selected 7-mer peptides of M4 ChR that cross-react (in silico) to M3 ChR but not to M1 ChR (Table 2S). We then selected, among them, only those peptides that have surface exposure on their respective proteins (Table 1). The result is that patient sera react to extracellular loops 2 and 3 of both M3 and M4 ChRs (Figure 1), but also to a hidden antigen, a peptide of transmembrane helix 5 of both M3 and M4 ChR.

Our results are of interest because extracellular loops 2 and 3 of M3 ChR are known autoepitopes in Sjögren’s syndrome (Ss) (Deng C. et al. 2915). Moreover, sera from patients with orthostatic hypotension (OH) react to extracellular loop 2 of M3 ChR, where they show an agonistic effect, thus acting as vasodilators (Li H. et al. 2012). OH, a form of orthostatic intolerance has been reported in ME/CFS patients (Bou-Holaigah et al. 1995) while fatigue similar to post-exertional malaise has been described in Ss (Segal B. et al. 2008). A pathogenic role of these antibodies in fatigue for both ME/CFS and Sjögren syndrome could perhaps be due to their vasodilatory effect.

Our analysis unveiled reactivity to a hidden autoepitope, which belongs to transmembrane helix 5 of M3 and of M4 ChR. This epitope is buried inside the plasma membrane when these two receptors are in their physiological position, so this reactivity can’t contribute to the pathogenesis of ME/CFS.

None of the 7-mer peptides of M4 ChR that cross-react to M3 ChR and at the same time don’t cross-react to M1 ChR presents in silico reactivity to B2 AdR. This means that in those patients whose sera present reactivity to both M4-M3 ChR and B2 AdR, there are two distinct autoantibodies. This prediction of our model is consistent with the low correlation found by Loebel and colleagues between anti-M4 ChR and anti-B2 AdR antibodies (Loebel M et al. 2016).

Most B cells epitopes on non-denaturated proteins (i.e. proteins that conserve their tertiary structure) are believed to be conformational (Morris, 2007), so a significant limitation of this study is due to the fact that our analysis considers only linear epitopes. Nevertheless, the main limitation of this study remains by far my encephalopathy.


This analysis of previously published data suggests a role for the second and the third extracellular loop of M4 and M3 ChR as autoantigens in ME/CFS. It also predicts the presence of a hidden autoantigen and thus a risk of false-positive results with standard ELISA.  The eight peptides found by this analysis and their inverse sequences (Table 2) should be employed as query sequences for the search for possible triggering pathogens and for other autoantigens. These predictions should be tested using both cell-based assays and ELISA tests with these 8 peptides coated on the plate.

Table 2.PNG
Table 2. Peptides belonging to M4 and M4 ChR that fulfill our selection criteria are collected on the left. On the right, their reverse sequences. These 16 peptides can be used in BLAST in order to serach for triggering pathogens and for other possible autoepitopes.

Supplementary material. The following two tables represent the first two steps of the analysis presented in this paper. M4 ChR 7-mer peptides that are cross-reactive to M3 ChR are collected in Table 1S, while those of them that are non-cross-reactive (or borderline) to M1 ChR are collected in Table 2S.

Table 1S. Peptides of M4 ChR that are cross-reactive to M3 ChR are collected in the first column. In the second column are collected the scores of these 7-mer peptides obtained from the comparison with M1 ChR. For those that obtained a score below 60, the score from the comparison with B2 AdR is reported in column 5. Positions of peptides of interest that belong to M3ChR and B2 AdR are collected in columns 4 and 6 respectively.
Table 2S.PNG
Table 2S. These 20 peptides are those M4 ChR peptides that cross-react to M3 ChR and at the same time are non-cross-reactive or borderline when compared to M1 ChR. Reactivity to B2 AdR is also indicated, as well as positions with respect to the plasma membrane and mean protrusion indexes. On the left are indicated those peptides of M4 ChR that pass the selection according to our criteria. Both a strict selection and a selection with more weak criteria are reported.

Mark Davis and the search for the universal immune test

Mark Davis and the search for the universal immune test

A traslation of this blog post to Spanish can be downloaded here. I would like to thank Humbert.Cat for the translation.

1. Introduction

These are some notes about the talk that Mark Davis gave during the Community Symposium held in August at Stanford (video). I will introduce some basic notions about T cell receptors (TCR) in paragraphs 2, 3, 4, and 5. Paragraphs 6 is a description of an innovative technology developed by Mark Davis and his colleagues, based on information gathered from the video itself and three research papers published by Davis and others in the last 4 years. This background should be hopefully enough to allow a good understanding of the exciting pilot data presented by Mark Davis on T cell activity in ME/CFS (paragraph 7), and in chronic Lyme (paragraph 8), and to realize why this technology promises to be some sort of universal test for any kind of infectious and autoimmune diseases, known or unknown.

2. T cells

T cells are a type of leukocytes (also known as white blood cells), the cellular component of our immune system. Most of our circulating T cells are represented by T helper cells (Th cells) and cytotoxic T lymphocytes (CTL). While the function of Th cells is to regulate the activity of other leukocytes through the production of a wide range of chemicals (cytokines), CTLs are directly involved in the killing of host cells infected by pathogens. T cells belong to the adaptive arm of the immune system, along with B cells (the factories of antibodies), and as such, they are meant to provide a defence tailored to specific pathogens: our immune system can provide not only antibodies specific for a given pathogen but also specific T cells (both Th cells and CTLs). The innate arm of the immune system (which includes natural killer cells, macrophages, dendritic cells, mast cells…) on the other hand can provide only a one-fits-all type of defense, which represents the first line of immune response, during an infection.

3. T cell receptor

T cells search for their specific pathogens thanks to a molecule expressed on their surface, called T cell receptor (TCR). In figure 1 you can see a schematic representation of the TCR and of the mechanism by which T cells recognize their targets. Antigens (proteins) from pathogens are presented to T cells by other cells of our body: they are displayed on molecules called major histocompatibility complex (MHC), expressed on the outer membrane; if the antigen fits the TCR of a specific T cell, then this T cell is activated and proliferates (clonal expansion). The two chains (α and β) are assembled using the transcription of gene segments with several copies each: in other words, TCRs are assembled with peptides chosen randomly within a set of several possible choices. This leads to a repertoire of 10^15 possible different TCRs (Mason DA 1998). Each T cell displays only one type of TCR.

Figure 1. Upper half. Th cells and CTLs share the same TCR: in both cases this molecule is the assembly of two peptides (chain α and chain β), but while the TCR of Th cells (on the right) is expressed next to the molecule CD4 (which binds to class II MHC), the TCR of CTL is associated with the molecule CD8 (on the left), which is specific for MHC I. Black bars represent four chains (a complex called CD3) that are involved in the signaling of the TCR with the nucleus of the cell (by Paolo Maccallini). Lower half. A beautiful structural representation of the TCR, bound to the peptide-MHC complex (pMHC), from (Gonzàlez PA et al. 2013). In green the peptide, in blue the β chain, in dark green the α chain. CDRs (complementarity determining regions, orange) are composed of those residues of the α and β chains that directly bind the pMHC.

4. T helper cells

Th cells can recognize only antigens presented by class II MHC: this class of MHC is expressed on the outer membrane of some leukocytes, mainly dendritic cells, B cells, and macrophages (referred to as antigen presenting cells, APCs). MHC II engages the TCR of Th cells thanks to peptide CD4 (expressed exclusively by Th cells). The antigen presented by MHC II is a peptide with a length of 13-17 amino acids (Rudensky, et al., 1991) (figure 2).

Figure 2. The TCR expressed by a Th cell binds an epitope presented by a class II MHC expressed on the plasma membrane of an APC. Chains α and β of MHC II are also represented (by Paolo Maccallini).

5. Cytotoxic T lymphocytes

TCRs expressed by CTLs can bind only antigens displayed by class I MHC, which can be found on the outer membrane of any cell of our body. CD8 is the molecule that makes the TCR expressed by CTLs specific for MHC I. While antigens presented by APCs belongs to pathogens that have been collected on the battlefield of the infection, peptides displayed by class I MHC of a specific cell belong to pathogens that have entered the cell itself, therefore they are the proof of an ongoing intracellular infection (figure 3). When a CTL recognizes an antigen that fits its TCR, then the CTL induces apoptosis (programmed death) of the cell that displays it. Antigens presented by MHC I are peptides in the range of 8 to 10 amino acids (Stern, et al., 1994).

Figure 3. An infected cell displays a viral antigen on its MHC I. The TCR of a CTL binds this peptide and send a signal to the nucleus of the CTL itself, that responds with the induction of apoptosis (releasing granzymes, for instance) of the infected cell (by Paolo Maccallini).  

6. The universal immune testing

In his talk, Mark Davis presents an overview of some basic concepts about the immune system, before introducing his exciting new data about ME/CFS and post-treatment Lyme disease syndrome (PTLDS, also known as chronic Lyme). But he also describes a few details of a complex new assay that is theoretically able to read all the information packed in the repertoire of TCRs present – in a given moment – in the blood of a human being. As such, this test – that I have named the universal immune testing – seems to have the potential to determine if a given patient has an ongoing infection (and the exact pathogen) or an autoimmune disease (and the exact autoantigen, i.e. the tissue attached by the immune system). To my understanding, this assay requires three steps, described in the following sections.

6.1. First step: TCR sequencing

As said in paragraph 3, when T cells encounter their specific peptide presented by MHC, they proliferate so that in blood of patients with an ongoing infection (or with a reaction against self, i.e. autoimmunity) we can find several copies of T cells expressing the same TCR: while in healthy controls about 10% of total CD8 T cells is represented by clones of a few different T cells (figure 4, first line), in early Lyme disease – an example of active infection – and in multiple sclerosis (MS) – an example of autoimmune disease – we have a massive clonation of a few lines of CTLs (figure 5, second and third line, respectively). The first step of the universal immune testing is represented by the identification of the exact sequence of TCRs expressed by T cells in blood, as reported in (Han A et al. 2014) where it is described how to sequence genes for the α and the β chain of a given T cell. This approach allows to build graphs of the kind in figure 4, and therefore to determine whether the patient has an abnormal ongoing T cell activity or not. If a clonal expansion is found, then we can speculate that either an active infection is present or some sort of autoimmune condition.

Clonal expansion CD8.png
Figure 4. Each circle represents a patient. In the first line, we have four healthy controls, with no clonal expansion of CD8 T cells (the first one, left) or with only a low-level clonal expansion (slices in blue, white, and grey). In the second line, we have four patients with active Lyme disease (early Lyme) and all of them present a massive expansion of only three different T cells (slices in red, blue and orange). In the third line, we have four MS patient with most of their CD8 T cells represented by clones of a bunch of T cells. From the talk by Mark Davis.

6.2. Second step: TCR clustering

Mark Davis and his group have been able to code a software that allows to identify TCRs that share the same antigen, either within an individual or across different donors. This algorithm has been termed GLIPH (grouping of lymphocyte interaction by paratope hotspots) and has been found capable – for instance – to cluster T CD4 cell receptors from 22 subjects with latent M. tuberculosis infection into 16 distinct groups, each of which comprises TCRs from at least 3 different donors (Glanville J et al. 2017). Five of these groups are reported in figure 5. The idea here is that TCRs that belong to the same cluster, react to the same peptide-MHC complex (pMHC).

Figure 5. Five group of TCRs from 22 different donors with latent tuberculosis, clustered by GLIPH. The first column on the left has TCRs IDs, the second one reports donors IDs. Complementarity determining regions (CDR) for the β and the α chains are reported in the third and fifth column, respectively. From (Glanville J et al. 2017).

6.3. Third step: quest for the epitope(s)

As we have seen, this new technology allows to recognize if T cell clonal expansion is an issue in a given patient, by sequencing TCRs from his peripheral blood. It also allows to cluster TCRs either within an individual or across different patients. The next step is to identify what kind of antigen(s) each cluster of TCRs reacts to. In fact, if we could recognize these antigens in a group of patients with similar symptoms, with T cell clonal expansion and similar TCRs, we would be able to understand whether their immune system is fighting a pathogen (and to identify the pathogen) or if it is attacking host tissues and, if that was the case, to identify what tissue. As mentioned, the number of possible TCR gene rearrangement is supposed to be about 10^15, but the number of possible Th cell epitope is about 20^15 which is more than 10^19. This implies that TCRs have to be cross-reactive to some extent, in order to recognize all possible peptides presented by MHCs (Mason DA 1998). The exact extent of this cross-reactivity and the mechanism by which it is obtained has been elucidated by Mark Davis and his colleagues in a recent paper (Birnbaum ME et al. 2014) that gives us the third step of the universal immune testing. The aim of this phase is to take a given TCR and to find the repertoire of his specific antigens (as said, one TCR reacts to several antigens). In order to understand how this is possible let’s consider one of the experiments described in the paper mentioned above. The researchers considered two well-defined TCRs (named Ob.1A12 and Ob.2F3), cloned from a patient with MS and known to recognize peptide 85-99 (figure 6) of myelin basic protein (MBP) presented by HLA-DR15. They then prepared a set of yeast cells expressing HLA-DR15 molecules, each presenting a different peptide of 14 amino acids, with fixed residues only at position 1 and 4, where the peptide is anchored to MHC (figure 6, left). When copies of Ob.1A12 are added to this culture of yeast cells expressing pMHC complexes, they bind only some of them, and as you can see in the right half of figure 6, for each position of the epitopes bound by Ob.1A12, there is an amino acid that is more likely: for instance, the typical epitope of Ob.1A12 preferentially has alanine (A) at position -4, histidine (H) at position -3, arginine (R) at position -2, and so forth. As you can see, histidine (H) at position 2 and phenylalanine (F) at position 3 are obligate amino acids for a Ob.1A12 epitope.

Figure 6. On the left: peptide 85-99 of myelin basic protein (first row) is known to be an epitope for Ob.1A12. At position 1 and 4 it has two residues that allow its binding to the MHC molecule. At position -2, -1, 2, 3, and 5 we find those residues that bind the TCR. The second row represents the generic epitope of the peptide library used to identify the degree of crossreactivity between all the possible Ob.1A12 specific epitopes. On the right: the likelihood of amino acids for each position of Ob.1A12 specific epitopes is represented by shades of violet. As you can see, histidine (H) at position 2 and phenylalanine (F) at position 3 are obligate amino acids for a Ob.1A12 epitope. From (Birnbaum ME et al. 2014).

The table on the right side of figure 6 is, in fact, a substitution matrix with dimension 14×20, a tool that can be used to scan the peptide database in order to find, among all the known peptides expressed by living creatures, all the possible Ob.1A12 specific epitopes. Substitution matrices are commonly used for the so-called peptide alignment, a technique that aims at the identification of similarities between peptides. These matrices are based on evolutionary considerations (Dayhoff, et al., 1978) or on the study of conserved regions in proteins (Henikoff, et al., 1992). But the search for specific epitopes of a given TCR requires (as we have seen here for Ob.1A12) a substitution matrix built ad hoc for that TCR: each TCR requires its own substitution matrix that is obtained adding clones of that TCR on a culture of yeast cells presenting a huge peptide library on their MHCs, and analyzing data from this experiment. So, quite a complex process! In the case of Ob.1A12, this process led to 2330 peptides (including MBP), while the Ob.2F3 specific substitution matrix found 4824 epitopes within the whole peptide database. These peptides included both non-human proteins (bacterial, viral…) and human peptides. For 33 of them (26 non human and 7 human proteins), this group of researchers performed a test in order to directly verify the prediction: 25/26 of environmental peptides and 6/7 of the human peptides induced proliferation of T cells expressing Ob.1A12 and/or Ob.2F3, and this is a huge proof of the validity of this analysis! These 33 peptides are reported in figure 7. This is the last step of the universal immune testing, the one that from the TCR leads to the epitopes. As you have seen, a huge set of different peptides from different sources is linked to each single TCR, in other words, crossreactivity is an intrinsic property of TCR. This also means that lymphocyte transformation tests (LTTs), widely used in Europe for the detection of infections like Borrelia burgdorferi and others, bear a high risk of false-positive results and require a process of experimental and theoretical validation, that is currently lacking (see also this post on this issue).

Crossreactive epitopes.JPG
Figure 7. A set of 33 peptides (both human and environmental) predicted to be specific epitopes for both Ob.1A12 and Ob.2F3. From (Birnbaum ME et al. 2014).

We are now ready to fully appreciate the pilot data that Mark Davis presented at the Symposium on CD8 T cell clonal expansion in ME/CFS and in chronic Lyme.

7. We have a hit!

Mark Davis, along with Jacob Glanville and José Montoya, has sequenced TCRs from the peripheral blood of 50 ME/CFS patients and 49 controls (first step of the universal immune testing, remember?), then they have clustered them using the GLIPH algorithm (second step). They have found 28 clusters with more than 2500 similar sequences each, where each cluster collects multiple sequences from the same individual as well as (which is perhaps more important) sequences from different patients (figure 8). The cluster that I have circled in red, for instance, is a collection of more than 3000 similar TCRs. The presence of this wide clusters in ME/CFS patients, compared to healthy controls, represents an indirect proof of a specific T cell response to some common trigger in this group of patients, which might be a pathogen or a tissue of the body (or both).

Clustered TCR
Figure 8. In ME/CFS, TCRs sequences from 50 patients form 28 clusters with more than 2500 similar sequences, and this is clearly not the case in healthy controls. This point to some specific immune response to a pathogen or to a human tissue (or both). This slide is from the talk by Mark Davis.

Among these 50 ME/CFS patients, Davis and colleagues selected 6 patients with similar HLA genes (figure 9, left), 5 females among them, and studied their TCRs deeper. In the right half of figure 9, you can see for each patient the degree of CTL clonal expansion. Remember that in healthy controls only about 10% of CTLs is composed by clones of a few cells (figure 4, first raw), while here we see that about 50% of all CTLs is composed by clones. So, a “marked clonal expansion” of CD8 T cells, as Davis said.

ME subjects CD8
Figure 9. On the left: 6 MECFS patients with similar HLA genes have been selected. Patient ID is reported in the first column on the left, the second column indicates the age of each patient, the third indicates the gender, the fourth column is about exposure to cytomegalovirus, the third one is on MHC I genes. On the right: analysis of clonal expansion of CD8 T cells for each of the six patients. There is a marked clonal expansion (about 50%) compared to healthy controls (about 10%).

Sequences of α and β chains of TCRs from three of the six patients (patients L4-02, L4-10, and L3-20) are reported in figure 10 (I have verified that in fact these are sequences of α and β chains of human TCRs using them as query sequences in standard protein BLAST).

TCR epitope.png
Figure 10. Beta chains (first column) and respective α chains (fifth column) from 3 ME/CFS patients (L4-02, L4-10, and L3-20, last column).

So, we have seen so far the first two steps of the universal immune testing in ME. What about the third step? In his talk, Mark Davis didn’t present any particular epitope, he just showed a slide with what likely is the selection of the epitopes from the peptide library (see paragraph 6.3) by one of the TCRs reported in figure 10. This selection is reported in figure 11, but from that picture, it is not possible to gather any information about the identity of these epitopes. As you probably remember from paragraph 6.3, the analysis of the peptides selected by a TCR among the peptide library allows the identification of a substitution matrix that can be used to select all the possible epitopes of that specific TCR, from the peptide database. This last crucial step has to be performed yet, or it has been already performed, but Davis has not communicated the preliminary results during his talk. Recently new resources have been made available by Open Medicine Foundation, for this promising research to be further pursued, among other projects (R). The aim here, as already said, is to find the antigen that triggers this T cell response. As Mark Davis said, it might be an antigen from a specific pathogen (perhaps a common pathogen that comes and goes) that elicits an abnormal immune response which ends targeting some host tissue (microglia, for instance), thus leading to the kind of immune activation that has been recently reported by Mark Davis himself and others in ME/CFS (Montoya JG et al. 2017). The idea of a common pathogen triggering a pathologic immune response is not new in medicine, and rheumatic fever (RF) is an example of such a disease: RF is an autoimmune disease that attacks heart, brain and joints and is generally triggered by a streptococcal throat infection (Marijon E et al. 2012). The other possible avenue is, of course, that of an ongoing infection of some kind, that has yet to be detected. As said (see par. 6.1), CD8 T cell clonal expansion is present in both acute infections (like early Lyme disease) and autoimmune diseases (like MS) (figure 4), so we have to wait for the antigen identification if we want to understand if the CTLs activity is against a pathogen and/or against a host tissue.

Figure 11. In this picture, we can see the selection, through several rounds, of a bunch of peptides by a particular TCR from a ME patient. The selection takes place among a huge collection of peptides presented by HLA-A2 (MHC I) expressed by yeast cells. At each round the number of possible peptides is smaller.

8. Chronic Lyme does exist

It has probably been overlooked that in his talk, Mark Davis reported also very interesting data on post-treatment Lyme disease syndrome (PTLDS, also known as chronic Lyme disease). In particular, he found a marked clonal expansion in CD8 T cells of 4 PTLDS patients (about 40% of total CTLs) as reported in figure 12: consider that in this case, blue slices represent unique T cells, while all the other slices represent clones! All that has been said about CD8 clonal expansion in ME/CFS does apply in this case too: it might be the proof of an ongoing infection – perhaps the same B. burgdorferi, as suggested by several animal models (Embers ME et al. 2017), (Embers ME et al. 2012), (Hodzic E et al. 2008), (Yrjänäinen H et al. 2010) – or a coinfection (a virus?) or it could be the expression of an autoimmune reaction triggered by the initial infection. This has still to be discovered, running the complete universal immune testing, but what is already clear from figure 12 is that PTLDS is a real condition, with something really wrong going on within the immune response: chronic Lyme does exist.

Figure 12. CD8 T cells clonal expansion in four chronic Lyme patients: there is a marked clonal expansion that stands for T cell activity against a pathogen or against host tissue.

9. Conclusions

Mark Davis and other researchers have developed a complex assay that is able to sequence TCRs from patients, cluster them into groups of TCRs that react to the same antigens, and discover the antigens that triggered that particular T cell response. This assay is a kind of universal immune testing that is theoretically able to recognize if a person (or a group of patients) presents an immune response against a pathogen or against one of his own tissues (or both). This approach has already given pilot data on an ongoing CD8 T cell activity in ME/CFS patients and in chronic Lyme patients and will hopefully identify the trigger of this immune response in the near future. Whether ME/CFS is an ongoing infection, an autoimmune disease or both, the universal immune testing might be able to tell us. This new technology is for immunology, what whole genome sequencing is for genetics, or metabolomics is for molecular diseases: it doesn’t search for a particular pathogen or a particular autoimmune disease. No, it searches for all possible infections and immune disorders, even those that have yet to be discovered.


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Testing the lymphocyte transformation test for Lyme disease

Testing the lymphocyte transformation test for Lyme disease

In questo articolo dimostro che un test LTT per malattia di Lyme che utilizzi come uno degli antigeni la OspC (proteina integra) di B. burgdorferi sensu stricto può teoricamente risultare positivo (falso positivo) in soggetti con aumentata permeabilità intestinale.


Some lymphocyte transformation tests (LTT) popular in Europe for the diagnosis of Lyme disease, use full-length OspC of B. burgdorferi as one of their antigens and request a positive stimulation index against only one or two antigens, in order to be considered positive. In what follows, we demonstrate that, in the case of patients with gut bacteria translocation, such a test has a theoretical risk of false positive results.

Lymphocyte transformation test

Lymphocyte transformation test (LTT) is an assay which allows measuring the activity of peripheral blood Th cells against specific antigens. T cell activation starts shortly after infection, with T cells proliferation and the production of cytokines (such as INF-γ) which regulate the adaptive immune response (Sompayrac, 2012). As T cell response vanishes after the resolution of the infection (Kaech, et al., 2007), LTT may be useful in providing a proof of active infection. When an LTT assay is performed, Th cells from peripheral blood of a patient are exposed to proteins from a particular pathogen. If a significant reaction is noted, which could be either Th cells proliferation or INF-γ expression, the assay is considered positive and suggestive of an active infection by that particular pathogen. The response is expressed through a number, often referred to as stimulatory index (SI). In Lyme disease, several attempts have been made in order to obtain such a tool, either by T cells proliferation assays or by INF-γ measures (Dressler, et al., 1991), (Chen, et al., 1999), (Valentine-Thon, et al., 2007), (von Baehr, et al., 2012), (Callister, et al., 2016 May). Nevertheless, this procedure has not been fully recognized as useful at present and neither the European guidelines (Stanek, et al., 2011) nor the CDC (Centers for disease control and prevention, 2015) recommend the use of this kind of test.

Figure 1. Presentation of an antigen to a helper T cell by MHC II molecule.

Th cells activation and cross-reactive T cell epitopes

Th cells are activated when their T cell receptors (TCR) recognize a complementary antigen presented by MHC II molecules (see Figure 1) (Sompayrac, 2012). Peptides presented by MHC II to T helper cells are exclusively linear epitopes, and they have a length between 13 and 17 amino acids (Rudensky, et al., 1991). Various experiments have demonstrated that peptides with 5 identical amino acids in a sequence of 10 have good chances to represent cross-reactive T cell epitopes (Root-Bernstein, 2014). That said, the algorithm described above for the LTT test is not free from the risk of false positive results, as each protein used as antigen could present one or more linear epitopes of 10 amino acids which share at least 5 amino acids with some epitope of 10 amino acids from another pathogen. This risk is particularly high when the assay uses complete proteins as antigens, and when a high SI for only a few antigens is required in order to have a positive result of the test.

OspC and Pseudomonas aeruginosa

We have used BLAST from NCBI (National Library of Medicine), with OspC from Borrelia burgdorferi (strain ATCC 35210 / B31 / CIP 102532 / DSM 4680) identified by the swiss-prot ID Q07337 () as query sequence, settings being as follows: expected threshold of 10, BLOSUM62 as substitution matrix, and a word of 3 amino acids. We have built a custom database with the main Phyla of the human gut microbiome observed in a healthy population, which are Bacteroides, Firmicutes, Proteobacteria, Verrucomicrobia, Actinobacteria, Tenericutes, and Euryarchaeota (Giloteaux, et al., 2016). One of the possible matches that BLAST gives back is the following alignment between the query sequence and the outer membrane protein G (OprG) of Pseudomonas aeruginosa (PDB ID: 2X27):


As you can see, we have 6 identical amino acids in a peptide 10 amino acids long. This means that this peptide from Borrelia burgdorferi could theoretically bind a Th cell previously activated by P. aeruginosa. Peptide 111-120 from OspC is reported in Figure 2. Peptide 51-60 of OrpG is in Figure 3.  The 3D structure of OspC from B. burgdorferi strain B31 used for that picture has been experimentally determined with X rays and a resolution of 2,51 Å in 2001 (Kumaran, et al., 2001) and its MMDB ID is 15958 (). The conclusion from this data is that Th cells from a patient with an active infection by P. aeruginosa could proliferate and produce INF-γ when exposed to OspC from B. burgdorferi. In other words, a patient with an active P. aeruginosa infection would come out to have a positive LTT test for OspC.

Figure 2. Peptide 111-120 (in yellow) of OspC from B. burgdorferi (B31) is surface exposed.
Figure 3. Peptide 51-60 of OrpG from Pseudomonas aeruginosa.

Gut bacteria translocation

A disrupted mucosal barrier of the bowel, with consequent translocation of bacteria from the gut to the peripheral blood, has been described in patients with liver diseases (Zhu, et al., 2013), chronic HIV infection (Openshaw, 2009), Crohn’s disease (Wyatt, et al., 1993), and in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (Giloteaux, et al., 2016). In ME/CFS it has been possible, in particular, to demonstrate the translocation of Pseudomonas aeruginosa, among other gram-negative enterobacteria. In fact serum concentration of IgA against lipopolysaccharides from P. aeruginosa and other enterobacteria has been found to be significantly greater in ME/CFS patients than in normal volunteers (Maes, et al., 2007). Thus in ME/CFS patients the adaptive immune system usually reacts against pathogens which exit from the gut, and in particular, we know that it reacts against P. aeruginosa.


ME/CFS patients are among the main users of this kind of tests, because of the similarities between Lyme disease and the clinical picture of ME/CFS (Gaudino, et al., 1997). ME/CFS patients have a high prevalence of increased gut permeability and gut microbiome translocation (Giloteaux, et al., 2016), and their immune system reacts against P. aeruginosa in many cases (Maes, et al., 2007). Thus, each LTT for Lyme disease which uses full-length OspC from B. burgdorferi ss as the antigen could theoretically lead to a high rate of false positive results in this population of patients. The Lyme disease LTT discussed above, which is currently popular in Europe, is one of such tests. More researches are warranted in order to confirm or exclude the theoretical danger of cross-reaction of Lyme disease LTT with gut microbiome. Moreover, on the basis of what here presented, it might be possible to develop an LTT specific for the diagnosis of gut bacteria translocation.


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